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Just Machines

Public Affairs Quarterly 36 (2):163-183 (2022)

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  1. Algorithms and Autonomy: The Ethics of Automated Decision Systems.Alan Rubel, Clinton Castro & Adam Pham - 2021 - Cambridge University Press.
    Algorithms influence every facet of modern life: criminal justice, education, housing, entertainment, elections, social media, news feeds, work… the list goes on. Delegating important decisions to machines, however, gives rise to deep moral concerns about responsibility, transparency, freedom, fairness, and democracy. Algorithms and Autonomy connects these concerns to the core human value of autonomy in the contexts of algorithmic teacher evaluation, risk assessment in criminal sentencing, predictive policing, background checks, news feeds, ride-sharing platforms, social media, and election interference. Using these (...)
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  • Machine learning in healthcare and the methodological priority of epistemology over ethics.Thomas Grote - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.
    This paper develops an account of how the implementation of ML models into healthcare settings requires revising the methodological apparatus of philosophical bioethics. On this account, ML models are cognitive interventions that provide decision-support to physicians and patients. Due to reliability issues, opaque reasoning processes, and information asymmetries, ML models pose inferential problems for them. These inferential problems lay the grounds for many ethical problems that currently claim centre-stage in the bioethical debate. Accordingly, this paper argues that the best way (...)
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  • On Hedden's proof that machine learning fairness metrics are flawed.Anders Søgaard, Klemens Kappel & Thor Grünbaum - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.
    1. Fairness is about the just distribution of society's resources, and in ML, the main resource being distributed is model performance, e.g. the translation quality produced by machine translation...
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  • How I Would have been Differently Treated. Discrimination Through the Lens of Counterfactual Fairness.Michele Https://Orcidorg Loi, Francesco Https://Orcidorg Nappo & Eleonora Https://Orcidorg Vigano - 2023 - Res Publica 29 (2):185-211.
    The widespread use of algorithms for prediction-based decisions urges us to consider the question of what it means for a given act or practice to be discriminatory. Building upon work by Kusner and colleagues in the field of machine learning, we propose a counterfactual condition as a necessary requirement on discrimination. To demonstrate the philosophical relevance of the proposed condition, we consider two prominent accounts of discrimination in the recent literature, by Lippert-Rasmussen and Hellman respectively, that do not logically imply (...)
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  • Equalized Odds is a Requirement of Algorithmic Fairness.David Gray Grant - 2023 - Synthese 201 (3).
    Statistical criteria of fairness are formal measures of how an algorithm performs that aim to help us determine whether an algorithm would be fair to use in decision-making. In this paper, I introduce a new version of the criterion known as “Equalized Odds,” argue that it is a requirement of procedural fairness, and show that it is immune to a number of objections to the standard version.
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